Week 3: Data Visualization

`ggplot2`

In the lecture, we showed that statistics alone may lead to a misunderstanding of the data. Therefore, when working with new data, we should always make some visualizations to help us understand the data. A common way for plotting in R today is through ggplot2.

ggplot2 is an R package (located in tidyverse) for “decoratively creating graphics”

https://ggplot2.tidyverse.org/reference/

library(tidyverse)

Example: Hate crimes and income inequality

A FiveThirtyEight article published in 2017 claimed that higher rates of hate crimes were tied to greater income inequality.

https://fivethirtyeight.com/features/higher-rates-of-hate-crimes-are-tied-to-income-inequality/

FiveThirtyEight publishes their data sets - let’s investigate for ourselves.
Data set is posted in BlueLine. Download this data set, and save it to your computer.

Follow these steps to read the data into RStudio:

In the Environment tab, click “Import Dataset”. Since this is a CSV document, you want to import a text file.
Navigate to your CSV data set. Make sure that the first row contains column names.
Import the data.

Another way to do this:

Put the data file next to the Rmd file.
In the console, printread.csv(hate.crimes.csv) See ?read.csv for more information about this function

hate_crimes <- read.csv("./data/hate_crimes.csv")
glimpse(hate_crimes)

## Rows: 51
## Columns: 13
## $ state                                    <chr> "Alabama", "Alaska", "Arizona…
## $ region                                   <chr> "South", "West", "West", "Sou…
## $ median_income                            <int> 42278, 67629, 49254, 44922, 6…
## $ unemployment                             <dbl> 0.060, 0.064, 0.063, 0.052, 0…
## $ share_population_in_metro_areas          <dbl> 0.64, 0.63, 0.90, 0.69, 0.97,…
## $ share_population_with_high_school_degree <dbl> 0.821, 0.914, 0.842, 0.824, 0…
## $ share_non_citizen                        <dbl> 0.02, 0.04, 0.10, 0.04, 0.13,…
## $ share_white_poverty                      <dbl> 0.12, 0.06, 0.09, 0.12, 0.09,…
## $ gini_index                               <dbl> 0.472, 0.422, 0.455, 0.458, 0…
## $ share_non_white                          <dbl> 0.35, 0.42, 0.49, 0.26, 0.61,…
## $ share_voters_voted_trump                 <dbl> 0.63, 0.53, 0.50, 0.60, 0.33,…
## $ hate_crimes_splc                         <dbl> 0.12583893, 0.14374012, 0.225…
## $ hate_crimes_fbi                          <dbl> 1.8064105, 1.6567001, 3.41392…

head(hate_crimes)

##        state region median_income unemployment share_population_in_metro_areas
## 1    Alabama  South         42278        0.060                            0.64
## 2     Alaska   West         67629        0.064                            0.63
## 3    Arizona   West         49254        0.063                            0.90
## 4   Arkansas  South         44922        0.052                            0.69
## 5 California   West         60487        0.059                            0.97
## 6   Colorado   West         60940        0.040                            0.80
##   share_population_with_high_school_degree share_non_citizen
## 1                                    0.821              0.02
## 2                                    0.914              0.04
## 3                                    0.842              0.10
## 4                                    0.824              0.04
## 5                                    0.806              0.13
## 6                                    0.893              0.06
##   share_white_poverty gini_index share_non_white share_voters_voted_trump
## 1                0.12      0.472            0.35                     0.63
## 2                0.06      0.422            0.42                     0.53
## 3                0.09      0.455            0.49                     0.50
## 4                0.12      0.458            0.26                     0.60
## 5                0.09      0.471            0.61                     0.33
## 6                0.07      0.457            0.31                     0.44
##   hate_crimes_splc hate_crimes_fbi
## 1       0.12583893       1.8064105
## 2       0.14374012       1.6567001
## 3       0.22531995       3.4139280
## 4       0.06906077       0.8692089
## 5       0.25580536       2.3979859
## 6       0.39052330       2.8046888

A simple plot?

Basic Format of a Plot:

data and aesthetics + plot type + options

The data, aesthetics, and plot type are necessary to create a plot. For example, below is what happens when we just specify the data and aesthetics.

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi))

Variable type

After specifying the data and aesthetics, we need to decide the plot type. In order to do that, we need to know the variable type(s). There are two different ways to distinguish the variables.

By function:

response variable
explanatory variable

By value type:

continuous variable
categorical variable

Type of plots

1. Visualize one continous variable, usually for the response variable using histograms and density plots

ggplot(hate_crimes, aes(x=median_income)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(hate_crimes, aes(x=median_income)) +
  geom_histogram(bins = 10)

ggplot(hate_crimes, aes(x=median_income)) +
  geom_density()

ggplot(hate_crimes, aes(x=median_income)) +
  geom_density(fill = "blue", alpha = 0.5)

2. Visualize one categorical variable, usually for the response variable using a bar-plot

ggplot(hate_crimes, aes(x=region)) +
  geom_bar()

ggplot(hate_crimes, aes(x=region)) +
  geom_bar() +
  coord_flip()

3. Visualize two continous variables. Focus on showing the relation between them. Can be response variable + explanatroy variable. Can also be explanatory variable + explanatory variable.

(a) Scatterplots

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi)) +
  geom_point()

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi)) + 
  geom_text(aes(label=state))

(b) Line plots and Smooth Line Plots to connect the points in the scatterplot. The Smooth Line Plots help show the trend due to smoothness

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi)) + 
  geom_line()

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi)) + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

4. Visualize one continous variable using a box-plot

What is the difference between box-plot and histogram/density plot? Box-plot shows the median but not the distribution.

ggplot(hate_crimes, aes(x=median_income)) +
  geom_boxplot()

ggplot(hate_crimes, aes(y=median_income)) +
  geom_boxplot()

Try it for yourself

Suppose we are interested in the unemployment rate and want to see its distribution.
Suppose we want to show the relation between unemployment rate and median income.
Report an approximate median for the unemployment rate.

Multiple groups

What if we want to visualize one continuous variable and one categorical variable? Sometimes we want to compare the variable(s) across multiple groups. eg: compare median income across different region. Which plots can compare multiple group?

Theses are called side-by-side plots.

ggplot(hate_crimes, aes(x=median_income, group = region)) +
  geom_density()

ggplot(hate_crimes, aes(y=median_income, group = region)) +
  geom_boxplot()

Wait, how can I know which group is which group?

Include options like color and size

ggplot(hate_crimes, aes(y=median_income, x = region)) +
  geom_boxplot()

ggplot(hate_crimes, aes(y=median_income, group = region)) +
  geom_boxplot(aes(color = region))

ggplot(hate_crimes, aes(x=median_income, group = region)) +
  geom_density(aes(color = region))

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi, group = region)) +
  geom_point(aes(color = region))

ggplot(hate_crimes, aes(x=median_income, y=hate_crimes_fbi, group = region)) +
  geom_point(aes(color = region, size = unemployment))

Other options

Add x-axis, y-axis labels and title

ggplot(hate_crimes, aes(x=gini_index, y=hate_crimes_fbi)) +
  geom_point(aes(color=region, size=unemployment)) + 
  xlab('Gini Index') + 
  ylab('Hate Crimes (FBI, Sept. 2016)') +
  ggtitle('The relation between Gini Index and Hate Crime rate in 2016')

Instead of putting all groups information into one page, you can do by each panel.

ggplot(hate_crimes, aes(x=gini_index, y=hate_crimes_fbi)) +
  geom_point(aes(color=region, size=unemployment)) + 
  xlab('Gini Index') + 
  ylab('Hate Crimes (FBI, Sept. 2016)') + 
  ggtitle('The relation between Gini Index and Hate Crime rate in 2016')+
  facet_wrap(~region, nrow=1)

Try it for yourself

Show the unemployment rate across different region. Use color to indicate different regions.
Show the relation between unemployment rate and FBI hate crime rate. Use size to indicate gini index. Make sure to include axis labels and title.
Plot the distribution of gini index and put differnet region on different panel.

Weather patterns

The data set Weather contains data on weather-related variables for several world cities.

#install.packages(mosaicData)
library(mosaicData)
data(Weather)
glimpse(Weather)

## Rows: 3,655
## Columns: 25
## $ city          <chr> "Auckland", "Auckland", "Auckland", "Auckland", "Aucklan…
## $ date          <date> 2016-01-01, 2016-01-02, 2016-01-03, 2016-01-04, 2016-01…
## $ year          <dbl> 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 20…
## $ month         <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ day           <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ high_temp     <dbl> 68, 68, 77, 73, 69, 69, 71, 77, 69, 71, 75, 69, 71, 75, …
## $ avg_temp      <dbl> 65, 66, 72, 66, 62, 63, 66, 70, 66, 66, 67, 66, 66, 68, …
## $ low_temp      <dbl> 62, 64, 66, 60, 55, 57, 60, 64, 64, 62, 59, 62, 62, 62, …
## $ high_dewpt    <dbl> 64, 64, 70, 66, 55, 54, 59, 72, 68, 63, 61, 66, 61, 63, …
## $ avg_dewpt     <dbl> 60, 63, 67, 60, 52, 51, 54, 67, 61, 58, 58, 62, 57, 61, …
## $ low_dewpt     <dbl> 55, 61, 64, 54, 48, 46, 50, 59, 55, 55, 54, 59, 54, 59, …
## $ high_humidity <dbl> 100, 100, 100, 100, 82, 88, 83, 100, 100, 88, 94, 100, 8…
## $ avg_humidity  <dbl> 82, 94, 91, 76, 69, 65, 65, 92, 81, 76, 72, 87, 73, 80, …
## $ low_humidity  <dbl> 68, 88, 74, 53, 56, 46, 53, 83, 64, 64, 53, 78, 64, 65, …
## $ high_hg       <dbl> 30.15, 30.04, 29.80, 30.12, 30.21, 30.24, 30.24, 30.01, …
## $ avg_hg        <dbl> 30.09, 29.90, 29.73, 29.90, 30.14, 30.22, 30.13, 29.79, …
## $ low_hg        <dbl> 30.01, 29.80, 29.68, 29.77, 30.09, 30.18, 30.04, 29.62, …
## $ high_vis      <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
## $ avg_vis       <dbl> 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
## $ low_vis       <dbl> 4, 1, 1, 6, 6, 6, 6, 2, 4, 6, 6, 3, 6, 6, 6, 6, 6, 2, 6,…
## $ high_wind     <dbl> 21, 33, 18, 15, 13, 17, 22, 21, 18, 20, 17, 16, 18, 17, …
## $ avg_wind      <dbl> 15, 21, 12, 10, 7, 8, 12, 14, 11, 15, 10, 8, 13, 9, 7, 4…
## $ low_wind      <dbl> 28, 46, NA, NA, NA, 28, 25, 28, 29, NA, 26, NA, NA, NA, …
## $ precip        <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "…
## $ events        <chr> "Rain", "Rain", "Rain", "Rain", NA, NA, "Rain", "Rain", …

We want to study the average temperature. First, we want to see its distribution.

ggplot(Weather, aes(x=avg_temp)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(Weather, aes(x=avg_temp)) +
  geom_density()

ggplot(Weather, aes(y=avg_temp)) + 
  geom_boxplot()

The box-plot of average temperature across by different cities.

ggplot(Weather, aes(y=avg_temp, group=city)) + 
  geom_boxplot(aes(color=city))

ggplot(Weather, aes(y=avg_temp, x = city, group=city)) + 
  geom_boxplot(aes(color=city))

ggplot(Weather, aes(y=avg_temp, x = city, group=city)) + 
  geom_boxplot(aes(color=city)) + theme(legend.position = "none")

The average temperature may be related to date. How to show the relation between temperature and date?

ggplot(Weather, aes(x=date, y=avg_temp)) +
  geom_point()

Maybe different city has totally different trend on average temperature.

ggplot(Weather, aes(x=date, y=avg_temp)) +
  geom_point(aes(color=city))

Weather patterns in Beijing

Beijing <- Weather %>% filter(city=='Beijing')

ggplot(Beijing, aes(x=date, y=avg_temp)) +
  geom_point()

ggplot(Beijing, aes(x=date, y=avg_temp)) +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

ggplot(Beijing, aes(x=date, y=avg_temp)) +
  geom_smooth() +
  geom_point()

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Weather by month

Instead of the date, we can also use the month. If I want to show the relation between month and temperature.

ggplot(Beijing, aes(y=avg_temp, x = month)) + 
  geom_point()

ggplot(Beijing, aes(y=avg_temp, x = as.factor(month))) + 
  geom_point()

ggplot(Beijing, aes(x=avg_temp)) + 
  geom_density(fill='lightblue', alpha=0.5)

ggplot(Beijing, aes(x=avg_temp, group=month)) + 
  geom_density(aes(color=as.factor(month), fill=as.factor(month)), alpha=0.5)

ggplot(Beijing, aes(x=avg_temp, group=month)) +
  geom_density(aes(color=as.factor(month), fill=as.factor(month)), alpha=0.5) + 
  facet_wrap(~month, nrow=3)

We can also use the lowest temperature and highest temperature, instead of the average.

ggplot(Beijing, aes(x=low_temp, y=high_temp)) +
  geom_point(aes(color=as.factor(month)))

Weather events

What else we can explore? How about events?

ggplot(Beijing, aes(x=date, y=avg_temp)) +
  geom_point(aes(color=events))

ggplot(Beijing, aes(x=events)) +
  geom_bar() +
  coord_flip()

ggplot(Beijing, aes(x=events)) +
  geom_bar() +
  coord_flip() +
  facet_wrap(~year, nrow=2)

Week 3: Data Visualization

MTH 365: Introduction to Data Science

May 30, 2024

Recommended Reading

`ggplot2`

Example: Hate crimes and income inequality

A simple plot?

Variable type

Type of plots

1. Visualize one continous variable, usually for the response variable using histograms and density plots

2. Visualize one categorical variable, usually for the response variable using a bar-plot

3. Visualize two continous variables. Focus on showing the relation between them. Can be response variable + explanatroy variable. Can also be explanatory variable + explanatory variable.

(a) Scatterplots

(b) Line plots and Smooth Line Plots to connect the points in the scatterplot. The Smooth Line Plots help show the trend due to smoothness

4. Visualize one continous variable using a box-plot

Try it for yourself

Multiple groups

Include options like color and size

Other options

Try it for yourself

Weather patterns

Weather patterns in Beijing

Weather by month

Weather events